LAUSR

The paper revisits the question of how stock return comovement varies with volatility and market returns. I propose an eigenvalue-based measure of comovement implied by the state-dependent correlation matrix estimated using a novel multivariate semi-Markov-switching approach. I show that compared to a basic Markov-switching structure the refined model performs very well in terms of capturing the well-known stylized facts of stock returns such as volatility clustering. With a focus on large-cap stocks, I illustrate the significance of comovement differential across states and document the different comovement patterns in different industries. Although the financial sector tends to conform to the conventional sentiment that comovement is highest when market is down and volatile, the conclusion should be tempered with caution when applied to other industries. In some cases, it is the high return state that registers the highest comovement.